earticle

영어

In the evolving landscape of e-commerce, personalized marketing has become an indispensable strategy for enhancing customer engagement and sales. This paper introduces Smart Convergence Technology, a novel approach that leverages natural language processing (NLP) to analyze date and temporal information within purchase data. Unlike traditional recommendation systems that often overlook the nuanced role of temporal contexts, our method maintains date information in its natural language form to capture detailed temporal dynamics effectively. Utilizing a Transformer-based model, this study analyzes users' purchase histories alongside specific date data to generate tailored product recommendations. The integration of NLP allows for a more precise reflection of temporal contexts, which is crucial for crafting personalized marketing messages that resonate with customer's specific needs and moments. Experiments conducted using datasets from UK ecommerce and Instacart platforms demonstrate the superiority of our approach, showing significant improvements in customer engagement metrics. These findings underline the potential of incorporating detailed temporal data into recommendation systems, offering insights into more sophisticated, data-driven marketing strategies in the digital commerce space.